DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 6/29/26. Claims 1-20 are pending in this Office Action.
Objection to claims 1,6,8 and 15 has been withdrawn in response to applicant’s amendment that overcomes the objection.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
In view of applicant’s amendments and arguments, the 101 rejection of claims 1-20 has been withdrawn.
Applicant’s arguments with respect to 102 and 103 rejections have been considered and are moot in view of new ground of rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-9, 11-16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kale et al.,(US Patent Application Publication 2018/0052884 A1, hereinafter “Kale”) and further in view of Coimbra et al.(US Patent Application Publication 2019/0340200 A1, hereinafter “Coimbra”)
As to claim 1, Kale teaches a method comprising: providing, for display on a client device, an intelligent assistant interface comprising a selectable element for interacting with a large language model ; receiving, from the client device, an indication of a user interaction with the selectable element of the intelligent assistant interface;(Kale par [0096] teaches user enters text input)
determining a content item to provide to the client device in response to the user interaction by utilizing the large language model to analyze a knowledge graph defining relationships among content items and user accounts of a content management system (Kale par [0111] teaches during processing of a user query, the parsed input data elements from the user query may be matched against the dimensions in the knowledge graph to help match the user’s demands with the available supply of items. Kale par [0112] teaches the knowledge graph 808 may be based on the historical interaction of all users with an electronic marketplace over a period of time ) ;
Kale fails to expressly teach determining based on determining the content item to provide to the client device, a content type of the content item and modifying, in respone to determining the content type , one or both of a size or a shape of the intelligent assistant interface according to the content type to present a first area dedicated to an embedded web browser for displaying content items of the content type together with a second area dedicated to the selectable element for interacting with the large language model.
However, Coimbra teaches determining based on determining the content item to provide to the client device, a content type of the content item and modifying, in response to determining the content type , one or both of a size or a shape of the intelligent assistant interface according to the content type to present a first area dedicated to an embedded web browser for displaying content items of the content type together with a second area dedicated to the selectable element for interacting with the large language model. (Coimbra par [0019] teaches the interface may include web browser embedded into the client portion of the automated assistant. Coimbra par [0043] teaches automated assistant 120 may examine the contents of user interface input and engage in a dialog session in response to certain terms being present in the user interface input and/or based on other cues. In many implementations, automated assistant 120 may utilize speech recognition to convert utterances from users into text, and respond to the text accordingly, e.g., by providing search results, general information, and/or taking one or more responsive actions (e.g., playing media, launching a game, ordering food, etc.) (interprets as content type). Coimbra Fig. 5-6 show examples of the changes of the display interface)
Therefore , it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kale and Coimbra to achieve the claimed invention. One would have been motivated to make such combination to allow third party developers to provide content in a uniform manner.(Coimbra par [0010])
As to claim 2, Kale and Coimbra teach the method of claim 1, wherein providing the intelligent assistant interface comprises providing a floating panel for display on the client device, wherein the floating panel includes a query panel for entering text queries (Kale par [0096] teaches user enters text input)
and one or more action elements selectable for performing respective processes via computer applications. (Kale par [0127] teaches the prompt 1204 may thus announce “I round these sneakers:” and show images of specific items or item groups available for purchase. The affirmation may be verbal reply or a selection of a particular displayed item)
As to claim 3, Kale and Coimbra teach the method of claim 1, wherein receiving the indication of the user interaction with the selectable element comprises one or more of: receiving a selection of an action element for performing a predefined process utilizing an application installed on the client device or hosted on a server;
receiving a text question for generating a response utilizing the large language model; or receiving a workflow prompt for generating workflow content by performing multiple processes utilizing multiple applications housed on different servers connected by a network. (Kale par [0096] teaches user enters text input)
As to claim 4, Kale and Coimbra teach the method of claim 1, wherein determining the content item to provide in response to the user interaction comprises:
utilizing the large language model to determine an input intent by processing the user interaction from the client device (Kale par [0038] teaches the artificial intelligence framework 128 further includes a natural language understanding component that operates to extract user intent and various intent parameters) ; and
identifying the content item corresponding to the input intent by analyzing the knowledge graph to perform a predefined process via an application installed on the client device or hosted on a server. (Kale par [0115] teaches the NLU component 214 may deliver a concise knowledge graph 808, with dimensions having some relevance, to the dialog manager 216 along with the dominant object of user interest, user intent and related parameters)
As to claim 5, Kale and Coimbra teach the method of claim 1, wherein determining the content item to provide in response to the user interaction comprises: utilizing the large language model to determine an input intent by processing the user interaction from the client device (Kale par [0038] teaches the artificial intelligence framework 128 further includes a natural language understanding component that operates to extract user intent and various intent parameters); and
utilizing the large language model to generate a response by analyzing the knowledge graph to determine graph information corresponding to the input intent for the response. (Kale par [0115] teaches the NLU component 214 may deliver a concise knowledge graph 808, with dimensions having some relevance, to the dialog manager 216 along with the dominant object of user interest, user intent and related parameters)
As to claim 6, Kale and Coimbra teach the method of claim 1, wherein determining the content item to provide in response to the user interaction comprises: utilizing the large language model to determine an input intent by processing the user interaction from the client device (Kale par [0038] teaches the artificial intelligence framework 128 further includes a natural language understanding component that operates to extract user intent and various intent parameters);
generating workflow content based on the input intent by utilizing multiple computer applications to perform a sequence of successive processes that build on one another to generate the workflow content based on the knowledge graph. (Kale par [0080] teaches extracting a user intent is performed by the NLE component 214 by breaking down into multiple parts. Each of the various parts of the overall problem of extracting user intent may be processed by a particular sub-component of the NLE sometimes separately and sometimes in combination. Kale par [0081] teaches sub-component includes a knowledge graph)
As to claim 7, Kale and Coimbra teach the method of claim 1, wherein modifying the intelligent assistant interface comprises generating a hybrid assistant-browser interface that includes the first area dedicated to the embedded web browser for displaying the content items of the content type and a second area dedicated to the intelligent assistant interface that includes the selectable element for interacting with the large language model. (Coimbra Fig. 2 teaches client portion of automated assistant 108 and embedded browser 248)
As to claim 8, Kale teaches a system comprising: at least one processor; and a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor (Kale par [0024] teaches one or more processors) , cause the system to:
provide, for display on a client device, an intelligent assistant interface comprising a selectable element for interacting with a large language model, wherein the selectable element comprises one or more of a query panel for entering text queries or an action element selectable performing a process using a separate computer application (Kale par [0096] teaches user enters text input);
receive, from the client device, an indication of a user interaction with the selectable element of the intelligent assistant interface (Kale par [0096] teaches user enters text input);
determine a content item to provide to the client device in response to the user interaction by utilizing the large language model to analyze a knowledge graph defining relationships among content items and user accounts of a content management system (Kale par [0111] teaches during processing of a user query, the parsed input data elements from the user query may be matched against the dimensions in the knowledge graph to help match the user’s demands with the available supply of items. Kale par [0112] teaches the knowledge graph 808 may be based on the historical interaction of all users with an electronic marketplace over a period of time );
Kale fails to expressly teach determine based on determining the content item to provide to the client device, a content type of the content item and modifying, in response to determining the content type , one or both of a size or a shape of the intelligent assistant interface according to the content type to present a first area dedicated to an embedded web browser for displaying content items of the content type together with a second area dedicated to the selectable element for interacting with the large language model.
However, Coimbra teaches determining based on determining the content item to provide to the client device, a content type of the content item and modifying, in response to determining the content type , one or both of a size or a shape of the intelligent assistant interface according to the content type to present a first area dedicated to an embedded web browser for displaying content items of the content type together with a second area dedicated to the selectable element for interacting with the large language model. (Coimbra par [0019] teaches the interface may include web browser embedded into the client portion of the automated assistant. Coimbra par [0043] teaches automated assistant 120 may examine the contents of user interface input and engage in a dialog session in response to certain terms being present in the user interface input and/or based on other cues. In many implementations, automated assistant 120 may utilize speech recognition to convert utterances from users into text, and respond to the text accordingly, e.g., by providing search results, general information, and/or taking one or more responsive actions (e.g., playing media, launching a game, ordering food, etc.) (interprets as content type). Coimbra Fig. 5-6 show examples of the changes of the display interface)
Therefore , it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kale and Coimbra to achieve the claimed invention. One would have been motivated to make such combination to allow third party developers to provide content in a uniform manner.(Coimbra par [0010])
As to claim 9, Kale and Coimbra teach the system of claim 8, further comprising instructions that, when executed by the at least one processor, cause the system to generate the knowledge graph by:
determining the relationships among the content items and the user accounts according to account behavior for a particular user account of the content management system (Kale par [0112] teaches the knowledge graph 808 may be based on the historical interaction of all users with an electronic marketplace over a period of time. Kale par [0114] teaches dimensions of the knowledge graph may now comprise the categories attributes and attribute values provided by previous user’s query inputs) ;
arranging nodes representing the user accounts and the content items within the content management system separated by distances reflecting the relationships defined by the account behavior of the particular user account; and connecting the nodes with edges defined by the distances between the nodes. (Kale par [0113] teaches regardless of the availability inventory, the knowledge graph 808 characterizes the search behavior of users, e.g. how users are attempting to find relevant items)
As to claim 11, Kale and Coimbra teach the system of claim 10, further comprising instructions that, when executed by the at least one processor, cause the system to generate a new action element to add to the intelligent assistant interface for the repeated sequence of user interactions.(Kale par [0086] teaches the knowledge graph 808 may also use dominant (e.g., most frequently user -queried or most frequently occurring an item inventory) attributes pertaining to that item category , and dominant values for those attributes. Thus the NLE component 214 may provide as its output the dominant object, user intent , and knowledge graph 808 that is formulated along dimensions likely to be relevant to the user query)
As to claim 12, Kale and Coimbra teach the system of claim 8, further comprising instructions that, when executed by the at least one processor, cause the system to determine the content item to provide in response to the user interaction by: utilizing the large language model to determine an input intent by processing the user interaction from the client device (Kale par [0038] teaches the artificial intelligence framework 128 further includes a natural language understanding component that operates to extract user intent and various intent parameters; and utilizing the large language model to generate a response by analyzing the knowledge graph to determine graph information corresponding to the input intent for the response. (Kale par [0115] teaches the NLU component 214 may deliver a concise knowledge graph 808, with dimensions having some relevance, to the dialog manager 216 along with the dominant object of user interest, user intent and related parameters )
As to claim 13, Kale and Coimbra teach the system of claim 8, further comprising instructions that, when executed by the at least one processor, cause the system to modify the intelligent assistant interface in response to determining that the content item from the knowledge graph is located at a server location displayable via a browser interface. ( Kale par [0031] teaches web client 102 may access the intelligent personal assistance system 106 via web interface.. Kale Fig.12 and par [0121] teaches processing user input to generate suggestive prompts )
As to claim 14, Kale and Coimbra teach the system of claim 8, further comprising instructions that, when executed by the at least one processor, cause the system to modify the intelligent assistant by generating a hybrid assistant-browser interface that includes the first area dedicated to the embedded web browser for displaying the content items of the content and the second area dedicated to the intelligent assistant interface that includes the selectable element for interacting with the large language model. (Coimbra Fig. 2 teaches client portion of automated assistant 108 and embedded browser 248)
As to claim 15, Kale teaches a non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to: provide, for display on a client device, an intelligent assistant interface comprising a selectable element for interacting with a large language model; receive, from the client device, an indication of a user interaction with the selectable element of the intelligent assistant interface, wherein the user interaction comprises one or more of entering a text query or selecting an action element via the intelligent assistant interface; (Kale par [0096] teaches user enters text input);
determine a content item to provide to the client device in response to the user interaction by utilizing the large language model to analyze a knowledge graph defining relationships among content items and user accounts of a content management system (Kale par [0111] teaches during processing of a user query, the parsed input data elements from the user query may be matched against the dimensions in the knowledge graph to help match the user’s demands with the available supply of items. Kale par [0112] teaches the knowledge graph 808 may be based on the historical interaction of all users with an electronic marketplace over a period of time );
Kale fails to expressly teach determine based on determining the content item to provide to the client device, a content type of the content item and modifying, in response to determining the content type , one or both of a size or a shape of the intelligent assistant interface according to the content type to present a first area dedicated to an embedded web browser for displaying content items of the content type together with a second area dedicated to the selectable element for interacting with the large language model.
However, Coimbra teaches determining based on determining the content item to provide to the client device, a content type of the content item and modifying, in response to determining the content type , one or both of a size or a shape of the intelligent assistant interface according to the content type to present a first area dedicated to an embedded web browser for displaying content items of the content type together with a second area dedicated to the selectable element for interacting with the large language model. (Coimbra par [0019] teaches the interface may include web browser embedded into the client portion of the automated assistant. Coimbra par [0043] teaches automated assistant 120 may examine the contents of user interface input and engage in a dialog session in response to certain terms being present in the user interface input and/or based on other cues. In many implementations, automated assistant 120 may utilize speech recognition to convert utterances from users into text, and respond to the text accordingly, e.g., by providing search results, general information, and/or taking one or more responsive actions (e.g., playing media, launching a game, ordering food, etc.) (interprets as content type). Coimbra Fig. 5-6 show examples of the changes of the display interface)
Therefore , it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kale and Coimbra to achieve the claimed invention. One would have been motivated to make such combination to allow third party developers to provide content in a uniform manner.(Coimbra par [0010])
As to claim 16, Kale and Coimbra teach the non-transitory computer readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the at least one processor to provide the intelligent assistant interface by providing a floating panel for display on the client device, wherein the floating panel includes a query panel for entering text queries (Kale par [0096] teaches user enters text input and one or more action elements selectable for performing respective processes via computer applications. (Kale par [0127] teaches the prompt 1204 may thus announce “I round these sneakers:” and show images of specific items or item groups available for purchase. The affirmation may be verbal reply or a selection of a particular displayed item)
As to claim 19, Kale and Coimbra teach the non-transitory computer readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the at least one processor to modify the intelligent assistant interface to present the embedded web browser in response to determining that the content item from the knowledge graph is located at a server location displayable via a browser interface. ( Kale par [0031] teaches web client 102 may access the intelligent personal assistance system 106 via web interface.. Kale Fig.12 and par [0121] teaches processing user input to generate suggestive prompts )
As to claim 20, Kale and Coimbra teach the non-transitory computer readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the at least one processor to modify the intelligent assistant interface by generating a hybrid assistant-browser interface that includes the first area dedicated to the embedded web browser for displaying the content items of the content types and the second area dedicated to the intelligent assistant interface that includes the selectable element for interacting with the large language model. ( Coimbra Fig. 2 teaches client portion of automated assistant 108 and embedded browser 248)
Claims 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kale and Coimbra and further in view of Wang et al.(US Patent Application Publication 2013/0283283 A1, hereinafter “Wang”)
As to claim 10, Kale and Coimbra teach the system of claim 8 but fail to expressly teach further comprising instructions that, when executed by the at least one processor, cause the system to: learn a repeated sequence of user interactions over time; and automatically perform processes for the repeated sequence of user interactions without initiation by user input based on detecting a trigger event.
However, Wang teaches learn a repeated sequence of user interactions over time; and automatically perform processes for the repeated sequence of user interactions without initiation by user input based on detecting a trigger event.(Wang par [0042] teaches suppose user often activated a browser application to read news at 9.00am. In this case, based on application usage history, the prediction subsystem may identify that the user is very likely to activate the browser at 0.00am and thus the device may even automatically connect to the news website which the user often visited without manual activation by the user)
Therefore , it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kale and Coimbra and Wang to achieve the claimed invention. One would have been motivated to make such combination to increase the convenience for the user (Wang par [0008])
As to claim 17, see the above rejection of claim 10.
Claims 18 is rejected under 35 U.S.C. 103 as being unpatentable over Kale, Coimbra and Wang and further in view of Morales et al.(US Patent Application Publication 2024/0272920 A1, hereinafter “Morales”)
As to claim 18, Kale, Coimbra and Wang teach the non-transitory computer readable medium of claim 17 but fail teach further comprising instructions that, when executed by the at least one processor, cause the at least one processor to, based on automatically generating the content item, provide an edit option for editing the content item before providing to another client device.
However, Morales teaches cause the at least one processor to, based on automatically generating the content item, provide an edit option for editing the content item before providing to another client device.(Morales par [0092] teaches the instance of the individual content item may be editable via the automated communication)
Therefore , it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kale, Coimbra , Wang and Morales to achieve the claimed invention. One would have been motivated to make such combination to increase work productivity amongst all users.(Morales par [0003])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Rodriguez et al. US Patent Application Publication 2018/0367483 A1, par [0081] discloses “an embedded application can implement a web interface view to provide an embedded interface within the chat interface, where the view can display data from a web page and/or implement code that executes in connection with web pages (Javascript, CSS, HTML, etc.)”. Yue et al., US Patent Application Publication 2021/0192134 A1, par [0030] discloses a morphing interface system that predicts likely user intents based on partial user input and may provide suggested intents for display to the user at the client device such as prompts for user selection.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEN DUONG whose telephone number is (571)270-7335. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker Lamardo can be reached at 571-270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HIEN L DUONG/Primary Examiner, Art Unit 2147
Prosecution Insights